Signal Integration Measure for Seismic Data

ABSTRACT

A method and system of processing seismic data includes acquiring seismic data, determining a frequency spectrum for the seismic data and determining an amplitude value associated with a local minimum frequency from the frequency spectrum to obtain an IZ minimum-amplitude value and a minimum frequency range limit. A maximum frequency range limit is determined and an integration measure is determined from an integration of the area bounded by the IZ minimum-amplitude value and the amplitude values between the minimum frequency range limit and the maximum frequency range limit. The integration-measure is stored for display.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application No.60/806,455 filed 30 Jun. 2006 and U.S. Provisional Application No.60/938,497 filed 17 May 2007.

BACKGROUND OF THE DISCLOSURE

The disclosure is related to seismic exploration for oil and gas, andmore particularly to determination of the positions of subsurfacereservoirs.

Expensive geophysical and geological exploration investment forhydrocarbons is often focused in the most promising areas usingrelatively slow methods, such as reflection seismic data acquisition andprocessing. The acquired data are used for mapping potentialhydrocarbon-bearing areas within a survey area to optimize exploratorywell locations and to minimize costly non-productive wells.

The time from mineral discovery to production may be shortened if thetotal time required to evaluate and explore a survey area can be reducedby applying selected methods alone or in combination with othergeophysical methods. Some methods may be used as a standalone decisiontool for oil and gas development decisions when no other data isavailable.

Geophysical and geological methods are used to maximize production afterreservoir discovery as well. Reservoirs are analyzed using time lapsesurveys (i.e. repeat applications of geophysical methods over time) tounderstand reservoir changes during production. The process of exploringfor and exploiting subsurface hydrocarbon reservoirs is often costly andinefficient because operators have imperfect information fromgeophysical and geological characteristics about reservoir locations.Furthermore, a reservoir's characteristics may change as it is produced.

The impact of oil exploration methods on the environment may be reducedby using low-impact methods and/or by narrowing the scope of methodsrequiring an active source, including reflection seismic andelectromagnetic surveying methods. Various geophysical data acquisitionmethods have a relatively low impact on field survey areas. Low-impactmethods include gravity and magnetic surveys that maybe used to enrichor corroborate structural images and/or integrate with other geophysicaldata, such as reflection seismic data, to delineate hydrocarbon-bearingzones within promising formations and clarify ambiguities in lowerquality data, e.g. where geological or near-surface conditions reducethe effectiveness of reflection seismic methods.

SUMMARY

A method and system of processing seismic data includes acquiringseismic data, determining a frequency spectrum for the seismic data anddetermining an amplitude value associated with a local minimum frequencyfrom the frequency spectrum to obtain an IZ minimum-amplitude value anda minimum frequency range limit. A maximum frequency range limit isdetermined and an integration measure is determined from an integrationof the area bounded by the IZ minimum-amplitude value and the amplitudevalues between the minimum frequency range limit and the maximumfrequency range limit. The integration-measure is stored for display.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A and FIG. 1B illustrate two spectra for the vertical componentincluding spectra of the passive seismic wavefield (vertical surfacevelocities) in the frequency range from 0.5 to 7.4 Hz;

FIG. 2 illustrates an integration of frequency amplitude data above aminimum amplitude level as is used for determining an IZ value;

FIG. 3 is a flow chart of data processing for application of one or moreembodiments to seismic data;

FIG. 4 is a hydrocarbon potential map as developed based on the relativestrength of values derived from f(IZ) determinations;

FIG. 5 illustrates a flow chart for a non-limiting embodiment for analternative method for determining a representative value related to thestrength of a hydrocarbon signal;

FIG. 6A and FIG. 6B illustrate integrations for V/H values greater thanunity;

FIG. 7 illustrates various acquisition geometries which may be selectedbased on operational considerations;

FIG. 8 illustrates a flow chart for a method according to a non-limitingembodiment of the present disclosure that includes using passivelyacquired seismic data to determine four independent seismic attributesfor hydrocarbon tremor detection;

FIG. 9A illustrates seismic attribute parameter extractions from dataacquired over a known hydrocarbon reservoir;

FIG. 9B illustrates seismic attribute parameter extractions from dataacquired over an area where hydrocarbon potential is expected to be verylow or non-existent;

FIG. 10A illustrates seismic attributes from data acquired over a knownhydrocarbon reservoir;

FIG. 10B illustrates seismic attributes from data acquired over an areawhere hydrocarbon potential is expected to be very low or non-existent;and

FIG. 11 is diagrammatic representation of a machine in the form of acomputer system within which a set of instructions, when executed maycause the machine to perform any one or more of the methods andprocesses described herein.

DETAILED DESCRIPTION

Information to determine the location of hydrocarbon reservoirs may beextracted from naturally occurring seismic waves and vibrations measuredat the earth's surface using passive seismic data acquisition methods. Amethodology for determining seismic attributes associated withreservoirs and for locating positions of subsurface reservoirs may bebased on covariance algorithms of continuous time series measurements ofthree-component seismic data. Seismic wave energy emanating fromsubsurface reservoirs, or otherwise altered by subsurface reservoirs, isdetected by three-component sensors and the polarity characteristics andassociated seismic attributes of these data enable determining thelocation of the source of the energy.

So called “passive” seismic data acquisition methods rely on seismicenergy from sources not directly associated with the data acquisition.In passive seismic monitoring there may be no actively controlled andtriggered source. Examples of sources recorded that may be recorded withpassive seismic acquisition are microseisms (e.g., rhythmically andpersistently recurring low-energy earth tremors), microtremors and otherambient or localized seismic energy sources.

Narrow-band, low-frequency microtremor signals have been observedworldwide over hydrocarbon reservoirs (oil, gas and water multiphasefluid systems in porous media). These low frequency “hydrocarbonmicrotremors” may possess remarkably similar spectral and signalstructure characteristics, pointing to a common source mechanism, eventhough the environments for the source of the microtremors may be quitedifferent.

Microtremors are attributed to the background energy normally present inthe earth. Microtremor seismic waves may include sustained seismicsignals within various frequency ranges. Microtremor signals, like allseismic waves, contain information affecting spectral signaturecharacteristics due to the media or environment that the seismic wavestraverse as well as the source of the seismic energy. These naturallyoccurring and often relatively low frequency background seismic waves(sometimes termed noise or hum) of the earth may be generated from avariety of sources, some of which may be unknown or indeterminate.

Characteristics of microtremor seismic waves in the “infrasonic” rangemay contain relevant information for direct detection of subsurfaceproperties including the detection of fluid reservoirs. The terminfrasonic may refer to sound waves below the frequencies of soundaudible to humans, and nominally includes frequencies under 20 Hz.

Three-component sensors are used to measure vertical and horizontalcomponents of motion due to background seismic waves at multiplelocations within a survey area. The sensors measure orthogonalcomponents of motion simultaneously.

Local acquisition conditions within a geophysical survey may affectacquired data results. Acquisition conditions impacting acquired signalsmay change over time and may be diurnal. Other acquisition conditionsare related to the near sensor environment. These conditions may beaccounted for during data reduction.

The sensor equipment for measuring seismic waves may be any type ofseismometer for measuring particle displacements or derivatives ofdisplacements. Seismometer equipment having a large dynamic range andenhanced sensitivity compared with other transducers, particularly inlow frequency ranges, may provide optimum results (e.g., multicomponentearthquake seismometers or equipment with similar capabilities). Anumber of commercially available sensors utilizing differenttechnologies may be used, e.g. a balanced force feed-back instrument oran electrochemical sensor. An instrument with high sensitivity at verylow frequencies and good coupling with the earth enhances the efficacyof the method.

Noise conditions representative of seismic waves that may have nottraversed subsurface reservoirs can negatively affect the recorded data.Techniques for removing unwanted noise and artifacts and artificialsignals from the data, such as cultural and industrial noise, areimportant where ambient noise is relatively high compared with desiredsignal energy.

The frequency ranges of hydrocarbon related microtremors for variousareas have been reported between ˜1 Hz to 10 Hz or greater. A direct andefficient detection of hydrocarbon reservoirs is of central interest forthe development of new and existing oil or gas fields. One approach isto identify the direction reservoir associated energy may be emanatingfrom by analyzing the polarity of three-component passive seismic data.If there is a steady source origin (or other alteration) oflow-frequency seismic waves within a reservoir, the reservoir attributesand the location of the reservoir may be determined using covarianceanalysis.

A sensor grid layout may be used with preselected node spacing ranging(e.g. from 100 to 1000 m, but in any case, survey dependent). FIG. 7illustrates some non-limiting embodiments of receiver station layouts.Several monitoring stations may be installed for the duration of theentire survey so that one or more sensors are more or less permanent forthe whole survey or longer.

The raw data may include strong perturbations (noises, artifacts) anddiscontinuities (data gaps). In order to obtain a clean signal in thetime domain, intervals with obvious strong artificial signals may beremoved. The power spectral density (PSD) may be determined from thecleaned raw data. One procedure is to determine the PSD for preselectedtime intervals to calculate the arithmetic average of each PSD for thewhole measurement time. This leads to a stable and reproducible resultin the frequency domain.

FIG. 1A and FIG. 1B illustrate two spectra for the vertical component ofthe passive seismic wavefield (vertical surface velocities) in thefrequency range from 0.5 to 7.4 Hz from two different sensor positionsin a survey area. The spectra illustrated as Record ID 70139 in FIG. 1Awas recorded over a known gas field, the spectra illustrated as RecordID 70575 in FIG. 1B is over an area with no expected hydrocarbonpotential.

A method of processing potential hydrocarbon microtremor data is to maplow-frequency energy anomalies in the expected total bandwidth of thehydrocarbon microtremor. This may be somewhere in a selected frequencyrange as illustrated in FIG. 2. As an example, the frequencies forhydrocarbon related tremors have been observed between 1 Hz and 10 Hz,though they may exist outside of this range as well. Analysis of thedata may lead to a selection of a restricted frequency range (e.g., 1 Hzto 3.7 Hz) for analysis. An integration technique considers a vectormeasurement representative of the strength of the hydrocarbon signal,for example the vertical component of the signal. The noise variationspresent in the spectra may be taken into account by determining anindividual frequency 205 associated with a PSD local amplitude minimumfor each spectrum between 1 and 1.7 Hz. Many hydrocarbon microtremorsare observed with a minimum in similar frequency ranges, though theywill be survey or area dependent. The integral of frequency amplitudedata above this minimum amplitude level is used for determining the “IZ”value (see FIG. 2; IZ stands for Integral of Z-component though inprincipal this method may be applied to any vector component orcombination of vector components). The symbol f(IZ) represents a measurerelated to an integration of the area between the amplitude value 201over the spectrum and the selected amplitude minimum 203 over the rangefrom a minimum frequency value 205 to a maximum frequency value 207. Foran example survey in an area with gas reservoirs the integral under thecurve was calculated between 1 and 3.7 Hz because of some identifiedartificial noise sources above this frequency interval.

FIG. 3 illustrates a flow chart for a non-limiting embodiment fordetermining a representative value related to the strength of ahydrocarbon signal. At least one vector component of seismic data isacquired 301 which may be the vertical vector of 3D data. Time intervalsare selected from the data for processing 303. These time intervals maybe selected based on the presence or characteristics of noise or signalin the time series to be used for processing. Any necessary sensorcalibration may be applied 305. For the selected time series, spectraover a selected frequency range is determined 307 and the spectra arestored 309. The spectra are then analyzed to determine a minimumfrequency value, which may be within a preselected range 311. A maximumfrequency value is selected for the upper IZ limit 313. A valuerepresentative over the range from the minimum value location to themaximum value location, for example the shaded area f(IZ) of FIG. 2, maybe determined relative to the area under the amplitude curve 201 andabove the amplitude minimum 203 between a selected frequency minimum 205and a selected frequency maximum 207. The selected frequency minimum isillustrated as selected over a preselected range (1 to 1.7 Hz in thisFIG. 2 example) but may be selected arbitrarily as well. The frequencymaximum (upper range endpoint) for inclusion in the integration may alsobe selected at a position of a local frequency minimum or maximum over arange, or the maximum may be selected arbitrarily as illustrated in thisexample.

A local frequency minimum suitable for demarking the lower rangeendpoint may be found by selecting the local minimum greater than thewell known ocean wave peak(s) that are very often found in the 0.1 to0.2 Hz area. The local minimum then often occurs in the vicinity of 1 to2 Hz and will occur before a general or temporary increase in thefrequency amplitudes for PSDs of the transformed seismic data. Thislocal minimum may be described then as the local minimum at a frequencygreater than the ocean wave peak frequency that may occur in the 0.8 to2 Hz frequency range or prior to any significant increase in amplitude.

A hydrocarbon potential map, FIG. 4, may be developed based on therelative strength of this values derived from f(IZ) determinations. Thelarger values or ‘Strong signal’ f(IZ) values have been found to be wellaligned with areas where gas production is located.

FIG. 5 illustrates a flow chart for a non-limiting embodiment for analternative method for determining a representative value related to thestrength of a hydrocarbon signal. Three vector components of seismicdata are acquired 501. Time intervals are selected for processing 503.These time intervals may be selected based on the presence of noise orsignal present in the time series to be used for processing. The spectrafor each vector, like Z(f), N(f) and E(f) are determined 505. A V/Hvalue is then calculated 507 by:${V/{H(f)}} = {\frac{Z(f)}{\sqrt{\frac{{N^{2}(f)} + {E^{2}(f)}}{2}}}.}$

A frequency range for the V/H data is selected 509 for analysis. Asillustrated in FIG. 6A and FIG. 6B, when this V/H value is greater thanunity, it may be integrated between unity and the amplitude values toobtain a V/H integrated value (VHI) 511 over a selected frequency range(e.g., F_(min) to F_(max) in FIG. 6A and FIG. 6B). The VHI values may bestored 515 and plotted as a map as hydrocarbon indicators or ahydrocarbon potential map. Alternatively, a maximum amplitude value ofthe V/H values, A_(max), or the difference between A_(max) and 1, may bedetermined 513 for a record and then plotted directly referenced to thesensor position that recorded the data. These values may be used to forma hydrocarbon map of a survey in a similar manner to FIG. 4. The maximumamplitude V/H values, A_(max), may be stored 517 and plotted as a map ofthe relative strength of possible hydrocarbon indicators. The values ofthe frequency at which A_(max) occurs, F(A_(max)) as illustrated in FIG.6B may also be stored and plotted.

Data may be acquired with arrays, which may be 2D or 3D, or evenarbitrarily positioned sensors 701 as illustrated in FIG. 7. FIG. 7illustrates various acquisition geometries which may be selected basedon operational considerations. Array 720 is a 2D array and whileillustrated with regularly spaced sensors 701, regular distribution isnot a requirement. Array 730 and 740 are example illustrations of 3Darrays. Sensor distribution 750 could be considered and array ofarbitrarily placed sensors and may even provide for some modification ofpossible spatial aliasing that can occur with regular spaced sensor 701acquisition arrays. Use of arrays enables a beam type migration tolocate and image source points.

FIG. 8 illustrates a method according to a non-limiting embodiment ofthe present disclosure that includes using passively acquired seismicdata to determine four independent seismic attributes for hydrocarbontremor detection. The embodiment, which may include one or more of thefollowing (in any order), includes acquiring three component passiveseismic data 801. The acquired data from each sensor station may be timestamped and include multiple data vectors. An example is passive seismicdata, such as three component data from “earthquake” type sensors. Eachdata vector is associated with an orthogonal direction of movement. Thevector data may be arbitrarily mapped or assigned to any coordinatereference system, for example designated east, north and depth (e.g.,respectively, Ve, Vn and Vz) or designated V_(x), V_(y) and V_(z)according to any desired convention. The data vectors may all be thesame length and/or synchronized.

While data may be acquired with multi-component earthquake seismometerequipment with large dynamic range and enhanced sensitivity,particularly for low frequencies, many different types of sensorinstruments can be used with different underlying technologies andvarying sensitivities. Sensor positioning during recording may vary,e.g. sensors may be positioned on the ground, below the surface or in aborehole. The sensor may be positioned on a tripod or rock-pad. Sensorsmay be enclosed in a protective housing for ocean bottom placement.Wherever sensors are positioned, good coupling results in better data.Recording time may vary, e.g. from minutes to hours or days. In generalterms, longer-term measurements may be helpful in areas where there ishigh ambient noise and provide extended periods of data with fewer noiseproblems.

The layout of a data survey may be varied, e.g. measurement locationsmay be close together or spaced widely apart and different locations maybe occupied for acquiring measurements consecutively or simultaneously.Simultaneous recording of a plurality of locations (a sensor array) mayprovide for relative consistency in environmental conditions that may behelpful in ameliorating problematic or localized ambient noise notrelated to subsurface characteristics of interest. Additionally thearray may provide signal differentiation advantages due to commonalitiesand differences in the recorded signal.

The data may be optionally conditioned or cleaned as necessary 803 toaccount for unwanted noise or signal interference. For example, variousprocessing methods may be employed such as offset removal, detrendingthe signal and a preliminary band pass or other targeted frequencyfiltering. The vector data may be divided into selected time windows 805for processing. The length of time windows for analysis may be chosen toaccommodate processing or operational concerns.

If a preferred or known range of frequencies for which a hydrocarbonmicrotremor signature is known or expected, an optional frequency filter(e.g., zero phase, Fourier of other wavelet type) may be applied 807 tocondition the data for processing. Examples of basis functions forfiltering or other processing operations include without limitation theclassic Fourier transform or one of the many Continuous WaveletTransforms (CWT) or Discreet Wavelet Transforms. Examples of othertransforms include Haar transforms, Haademard transforms and WaveletTransforms. The Morlet wavelet is an example of a wavelet transform thatoften may be beneficially applied to seismic data. Wavelet transformshave the attractive property that the corresponding expansion may bedifferentiable term by term when the seismic trace is smooth.Additionally, signal analysis, filtering, and suppressing unwantedsignal artifacts may be carried out efficiently using transforms appliedto the acquired data signals.

The three component data may be input to a covariance matrix 808 todetermine eigenvectors and eigenvalues to extract polarization relatedparameters of the recorded microtremor data. For example, as anon-limiting example, a zero-phase filter may be applied which selectsfrequencies from 1 Hz to 3.7 Hz for further analysis. Other ranges maybe selected on a case dependent basis (e.g., 1.5 Hz to 5.0 Hz). As afurther example, the analysis of the polarization behavior may beperformed for a plurality of preselected time intervals, such asconsecutive 40 second time intervals over an arbitrary length ofrecording.

Considering any time interval of three-component data u_(x), u_(y) andu_(z) containing N time samples auto- and cross-variances can beobtained with:$C_{ij} = \lbrack {\frac{1}{N}{\sum\limits_{s = 1}^{N}{{u_{i}(s)}{u_{j}(s)}}}} \rbrack$where i and j represent the component index x, y, z and s is the indexvariable for a time sample. The 3×3 covariance matrix:

C_(xx) C_(xy) C_(xz)

-   C=C_(xy) C_(yy) C_(yz)

C_(xz) C_(yz) C_(zz)

is real and symmetric and represents a polarization ellipsoid with abest fit to the data. The principal axis of this ellipsoid can beobtained by solving C for its eigenvalues λ₁≧λ₂≧λ₃ and eigenvectors p1,p2, p3:(C−λI)p=0where I is the identity matrix.

Inverting field-acquired passive seismic data to determine the locationof subsurface reservoirs may include using the acquired time-series dataas ‘sources’ which affect seismic parameters that may be determinedusing a covariance matrix analysis 809. At least four seismic parametersmay be extracted from the continuous signal of passive three-componentseismic data. The parameters include rectilinearity, dip, azimuth andstrength of signal.

The seismic data parameter called rectilinearity L, which also may becalled linearity, relates the magnitudes of the intermediate andsmallest eigenvalue to the largest eigenvalue${L = {1 - ( \frac{\lambda_{2} + \lambda_{3}}{2\quad\lambda_{1}} )}},$and measures the degree of how linearly the incoming wavefield ispolarized. This parameter yields values between zero and one. Twopolarization parameters describe the orientation of the largesteigenvector p₁=(p₁(x), p₁(y), p₁(z)) in dip and azimuth. The dip can becalculated with$\phi = {\arctan( \frac{p_{1}(z)}{\sqrt{{p_{1}^{2}(x)} + {p_{1}^{2}(y)}}} )}$and is zero for horizontal polarization and is defined positive inpositive z-direction. The azimuth is specified as$\theta = {\arctan( \frac{p_{1}(y)}{p_{1}(x)} )}$and measured positive counterclockwise (ccw) from the positive x-axis.In addition we analyse the strength of the signal which is given by theeigenvalue λ_(1:)λ₁ =√{square root over (p₁ ²(x)+p₁ ²(y)+p₁ ²(z).)}

A non-limiting example of a data display with representations of thefour seismic parameters is illustrated in FIG. 9A wherein field dataused for parameter extraction has been acquired over a known hydrocarbonreservoir. The field data in FIG. 9B used for seismic parameterextraction according to the present disclosure has been acquired over anarea where hydrocarbon potential is expected to be very low ornon-existent. All four attributes are schematically illustrated in FIG.9A and FIG. 9B, a polarization parameter sketch showing the dip andazimuth as marked. FIG. 9A illustrates a record that has highrectilinearity and medium dip, FIG. 9B shows low rectilinearity andrelatively high dip. The length of the dip vector 901 in FIG. 9A and 907in FIG. 9B is given by their largest eigenvalues, λ₁, respectively,further referred to as the strength of the signal. The azimuth isrepresented by graphically by vector 903 in FIG. 9A and 909 in FIG. 9B.A qualitative view of the rectilinearity of the measurements is depictedby 905 of FIG. 9A and 911 of FIG. 9B.

The trends in the attributes dip, azimuth, rectilinearity and strengthin a preselected frequency, that appears to be a hydrocarbon microtremorfrequency range (i.e., 1-3.7 Hz), for the data records illustrated inFIG. 1A (Record ID 70139) and FIG. 1B (Record ID 70565) is illustratedwith FIG. 10A and FIG. 10B.

In the reservoir area (Record ID 70139) the dip parameter has a stableand high value (≧80°) directly above the reservoir (FIG. 10A, top, lefthand side). The signal strength parameter is varying but clearly presentover the whole measured period. The rectilinearity is relatively highand relatively stable and appears to be correlated with the trend of thestrength attribute. The azimuth parameter is strongly varying as isexpected for such high dip parameters values.

For data from the recording station presumed to be outside of an areacontaining hydrocarbons (Record ID 70575) the dip parameter is fairlystable around low values (≈20°), as illustrated in FIG. 10B, top, lefthand side. The strength parameter is relatively low with some spikes.The rectilinearity parameter is lower in comparison with the valuesobserved above a hydrocarbon reservoir (such as illustrated in FIG.10A). The azimuth parameter is relatively stable.

FIG. 11 is illustrative of a computing system and operating environmentfor implementing a general purpose computing device in the form of acomputer 10. Computer 10 includes a processing unit 11 that may include‘onboard’ instructions 12. Computer 10 has a system memory 20 attachedto a system bus 40 that operatively couples various system componentsincluding system memory 20 to processing unit 11. The system bus 40 maybe any of several types of bus structures using any of a variety of busarchitectures as are known in the art.

While one processing unit 11 is illustrated in FIG. 11, there may be asingle central-processing unit (CPU) or a graphics processing unit(GPU), or both or a plurality of processing units. Computer 10 may be astandalone computer, a distributed computer, or any other type ofcomputer.

System memory 20 includes read only memory (ROM) 21 with a basicinput/output system (BIOS) 22 containing the basic routines that help totransfer information between elements within the computer 10, such asduring start-up. System memory 20 of computer 10 further includes randomaccess memory (RAM) 23 that may include an operating system (OS) 24, anapplication program 25 and data 26.

Computer 10 may include a disk drive 30 to enable reading from andwriting to an associated computer or machine readable medium 31.Computer readable media 31 includes application programs 32 and programdata 33.

For example, computer readable medium 31 may include programs to processseismic data, which may be stored as program data 33, according to themethods disclosed herein. The application program 32 associated with thecomputer readable medium 31 includes at least one application interfacefor receiving and/or processing program data 33. The program data 33 mayinclude seismic data acquired according to embodiments disclosed herein.At least one application interface may be associated with calculating aratio of data components, which may be spectral components, for locatingsubsurface hydrocarbon reservoirs.

The disk drive may be a hard disk drive for a hard drive (e.g., magneticdisk) or a drive for a magnetic disk drive for reading from or writingto a removable magnetic media, or an optical disk drive for reading fromor writing to a removable optical disk such as a CD ROM, DVD or otheroptical media.

Disk drive 30, whether a hard disk drive, magnetic disk drive or opticaldisk drive is connected to the system bus 40 by a disk drive interface(not shown). The drive 30 and associated computer-readable media 31enable nonvolatile storage and retrieval for application programs 32 anddata 33 that include computer-readable instructions, data structures,program modules and other data for the computer 10. Any type ofcomputer-readable media that can store data accessible by a computer,including but not limited to cassettes, flash memory, digital videodisks in all formats, random access memories (RAMs), read only memories(ROMs), may be used in a computer 10 operating environment.

Data input and output devices may be connected to the processing unit 11through a serial interface 50 that is coupled to the system bus. Serialinterface 50 may a universal serial bus (USB). A user may enter commandsor data into computer 10 through input devices connected to serialinterface 50 such as a keyboard 53 and pointing device (mouse) 52. Otherperipheral input/output devices 54 may include without limitation amicrophone, joystick, game pad, satellite dish, scanner or fax,speakers, wireless transducer, etc. Other interfaces (not shown) thatmay be connected to bus 40 to enable input/output to computer 10 includea parallel port or a game port. Computers often include other peripheralinput/output devices 54 that may be connected with serial interface 50such as a machine readable media 55 (e.g., a memory stick), a printer 56and a data sensor 57. A seismic sensor or seismometer for practicingembodiments disclosed herein is a nonlimiting example of data sensor 57.A video display 72 (e.g., a liquid crystal display (LCD), a flat panel,a solid state display, or a cathode ray tube (CRT)) or other type ofoutput display device may also be connected to the system bus 40 via aninterface, such as a video adapter 70. A map display created fromspectral ratio values as disclosed herein may be displayed with videodisplay 72.

A computer 10 may operate in a networked environment using logicalconnections to one or more remote computers. These logical connectionsare achieved by a communication device associated with computer 10. Aremote computer may be another computer, a server, a router, a networkcomputer, a workstation, a client, a peer device or other common networknode, and typically includes many or all of the elements describedrelative to computer 10. The logical connections depicted in FIG. 11include a local-area network (LAN) or a wide-area network (WAN) 90.However, the designation of such networking environments, whether LAN orWAN, is often arbitrary as the functionalities may be substantiallysimilar. These networks are common in offices, enterprise-wide computernetworks, intranets and the Internet.

When used in a networking environment, the computer 10 may be connectedto a network 90 through a network interface or adapter 60. Alternativelycomputer 10 may include a modem 51 or any other type of communicationsdevice for establishing communications over the network 90, such as theInternet. Modem 51, which may be internal or external, may be connectedto the system bus 40 via the serial interface 50.

In a networked deployment computer 10 may operate in the capacity of aserver or a client user machine in server-client user networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. In a networked environment, program modulesassociated with computer 10, or portions thereof, may be stored in aremote memory storage device. The network connections schematicallyillustrated are for example only and other communications devices forestablishing a communications link between computers may be used.

In one embodiment a method and system of processing seismic datacomprises acquiring seismic data, determining a frequency spectrum forthe seismic data and determining an amplitude value associated with alocal minimum frequency from the frequency spectrum to obtain an IZminimum-amplitude value and a minimum frequency range limit. A maximumfrequency range limit is determined and an integration measure isdetermined from an integration of the area bounded by the IZminimum-amplitude value and the amplitude values between the minimumfrequency range limit and the maximum frequency range limit. Theintegration-measure is stored for display.

In another aspect determining the IZ minimum-amplitude value includesselecting the local frequency minimum within a predetermined frequencyrange. Determining the maximum frequency range limit may includeselecting a frequency associated with an amplitude minimum. A localamplitude minimum for determining the maximum frequency range limit maybe selected in a predetermined frequency range. A map may be generatedusing the integration measure and the integration measures associatedwith geographic locations associated with the acquired seismic data. Theseismic data for processing may include a plurality of time intervals.Filtering, including a zero-phase band pass filter may be applied to theseismic data. The integration-measure may be displayed as a map or on adisplay device.

In another embodiment a set of application program interfaces isembodied on a computer readable medium for execution on a processor inconjunction with an application program for determining anintegration-measure for displaying a hydrocarbon potential indicatordetermined from seismic data includes a first interface that receivesdata to determine a frequency spectrum. A second interface receives anamplitude value associated with a local minimum frequency from thefrequency spectrum to obtain an IZ minimum-amplitude value and a minimumfrequency range limit. A third interface receives a maximum frequencyrange limit a fourth interface receives an integration-measure from theintegration of the area bounded by the IZ minimum-amplitude value andthe amplitude values between the minimum frequency range limit and themaximum frequency range limit.

In another aspect the set of application interface programs may includea fifth interface that receives instruction data for applying azero-phase frequency filter to the seismic data. A sixth interface mayreceive instructions for selecting the local frequency minimum in apredetermined frequency range. A seventh interface may receiveinstruction data for selecting a frequency associated with an amplitudeminimum to determine the maximum frequency range limit. An eighthinterface may receive instruction data for forming a map of anintegration-measure associated with a location, which location isassociated with the acquired seismic data.

In another embodiment an information handling system for determining ahydrocarbon potential indicator from seismic data comprises a processorconfigured to determine an amplitude value associated with a localminimum frequency from a frequency spectrum of acquired seismic data toobtain an IZ minimum-amplitude value and a minimum frequency rangelimit. A processor is configured to determine an upper frequency rangelimit and hydrocarbon potential indicator from the integration of thearea bounded by the IZ minimum-amplitude value and the amplitude valuesbetween the minimum frequency range limit and the maximum frequencyrange limit. A computer readable medium stores the hydrocarbon potentialindicator for display.

In another aspect the information handling system processor may beconfigured to determine the IZ minimum amplitude value by selecting thelocal frequency minimum in a predetermined frequency range. A processormay be configured to determine the maximum frequency range limit byselecting a frequency associated with an amplitude minimum. A processormay be configured to generate a map of the potential hydrocarbonindicator associated with a geographic location, which location isassociated with the acquired seismic data. A processor may be configureddetermine the frequency spectrum from a plurality of time intervals. Aprocessor may be configured to apply a zero-phase band pass filter tothe acquired seismic data. A display device may be used for displayingthe hydrocarbon potential indicator.

While various embodiments have been shown and described, variousmodifications and substitutions may be made thereto without departingfrom the spirit and scope of the disclosure herein. Accordingly, it isto be understood that the present embodiments have been described by wayof illustration and not limitation.

1. A method of processing seismic data, comprising: a) acquiring seismicdata; b) determining a frequency spectrum for the seismic data; c)determining an amplitude value associated with a local minimum frequencyfrom the frequency spectrum to obtain an IZ minimum-amplitude value anda minimum frequency range limit; d) determining a maximum frequencyrange limit; e) determining an integration-measure from an integrationof the area bounded by the IZ minimum-amplitude value and the amplitudevalues between the minimum frequency range limit and the maximumfrequency range limit; and f) storing the integration-measure fordisplay.
 2. The method of claim 1 wherein determining the IZminimum-amplitude value further comprises selecting the local frequencyminimum in a predetermined frequency range.
 3. The method of claim 1wherein determining the maximum frequency range limit further comprisesselecting a frequency associated with an amplitude minimum.
 4. Themethod of claim 3 further comprising selecting the amplitude minimum ina predetermined frequency range.
 5. The method of claim 1 furthercomprising generating a map of the integration-measure associated with ageographic location, which location is associated with the acquiredseismic data.
 6. The method of claim 1 wherein the seismic data comprisea plurality of time intervals.
 7. The method of claim 1 furthercomprising applying a zero-phase band pass filter to the seismic data.8. The method of claim 1 further comprising forming a display of theintegration-measure.
 9. A set of application program interfaces embodiedon a computer readable medium for execution on a processor inconjunction with an application program for determining anintegration-measure for displaying a hydrocarbon potential indicatordetermined from seismic data comprising: a first interface that receivesdata to determine a frequency spectrum; a second interface that receivesan amplitude value associated with a local minimum frequency from thefrequency spectrum to obtain an IZ minimum-amplitude value and a minimumfrequency range limit; a third interface that receives a maximumfrequency range limit; and a fourth interface that receives anintegration-measure from the integration of the area bounded by the IZminimum-amplitude value and the amplitude values between the minimumfrequency range limit and the maximum frequency range limit.
 10. The setof application interface programs according to claim 9 furthercomprising: a fifth interface that receives instruction data forapplying a zero-phase frequency filter to the seismic data.
 11. The setof application interface programs according to claim 9 furthercomprising: a sixth interface that receives instruction for selectingthe local frequency minimum in a predetermined frequency range.
 12. Theset of application interface programs according to claim 9 furthercomprising: a seventh interface that receives instruction data forselecting a frequency associated with an amplitude minimum to determinethe maximum frequency range limit.
 13. The set of application interfaceprograms according to claim 9 further comprising: an eighth interfacethat receives instruction data for forming a map of anintegration-measure associated with a location, which location isassociated with the acquired seismic data.
 14. An information handlingsystem for determining a hydrocarbon potential indicator from seismicdata comprising: a) a processor configured to determine an amplitudevalue associated with a local minimum frequency from a frequencyspectrum of acquired seismic data to obtain an IZ minimum-amplitudevalue and a minimum frequency range limit; b) a processor configured todetermine an upper frequency range limit and hydrocarbon potentialindicator from the integration of the area bounded by the IZminimum-amplitude value and the amplitude values between the minimumfrequency range limit and the maximum frequency range limit; and c) acomputer readable medium storing the hydrocarbon potential indicator fordisplay.
 15. The information handling system of claim 14 wherein theprocessor is configured to determine the IZ minimum amplitude value byselecting the local frequency minimum in a predetermined frequencyrange.
 16. The information handling system of claim 14 wherein theprocessor is configured to determine the maximum frequency range limitby selecting a frequency associated with an amplitude minimum.
 17. Theinformation handling system of claim 14 wherein the processor isconfigured to generate a map of the potential hydrocarbon indicatorassociated with a geographic location, which location is associated withthe acquired seismic data.
 18. The information handling system of claim14 wherein the processor is configured determine the frequency spectrumfrom a plurality of time intervals.
 19. The information handling systemof claim 14 wherein the processor is configured to apply a zero-phaseband pass filter to the acquired seismic data.
 20. The informationhandling system of claim 14 further comprising a display device fordisplaying the hydrocarbon potential indicator.